Artificial intelligence for predicting 5G network performance

ABSTRACT

Computing environments employing Artificial Intelligence (AI) are disclosed for enabling network operators to optimize messaging performance, particularly 5G (and future 6G) messaging performance, in real-time. For example, AI structures can assist in the selection and optimization of modulation tables. Three development phases are described: network data acquisition including faults experienced under various network conditions, AI structure tuning for accurate prediction of performance, and implementation of an algorithm based on the AI structure. Network operators can use the algorithm to compare predicted performance metrics according to various operating conditions, such as available modulation tables, and thereby select operating parameters for improved message reliability and throughput. The algorithm can also be used to adjust network variables, such as particular amplitude or phase levels in modulation tables.

PRIORITY CLAIMS AND RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/113,420, entitled “Wireless Modulation forMitigation of Noise and Interference”, filed Nov. 13, 2020, and U.S.Provisional Patent Application No. 63/151,270, entitled “WirelessModulation for Mitigation of Noise and Interference”, filed Feb. 19,2021, and U.S. Provisional Patent Application No. 63/157,090, entitled“Asymmetric Modulation for High-Reliability 5G Communications”, filedMar. 5, 2021, and U.S. Provisional Patent Application No. 63/159,195entitled “Asymmetric Modulation for High-Reliability 5G Communications”,filed Mar. 10, 2021, and U.S. Provisional Patent Application No.63/159,238 entitled “Selecting a Modulation Table to Mitigate 5G MessageFaults”, filed Mar. 10, 2021, and U.S. Provisional Patent ApplicationNo. 63/159,239 entitled “Artificial Intelligence for Predicting 5GNetwork Performance”, filed Mar. 10, 2021, all of which are herebyincorporated by reference in their entireties.

FIELD OF THE INVENTION

Disclosed herein are systems and methods for transmitting wirelessmessages, and more specifically for modulating wireless signals in 5G tomitigate noise and interference, selecting a particular modulationtable, and using AI (artificial intelligence) methods to mitigateobserved faults.

BACKGROUND OF THE INVENTION

In wireless networking, noise and interference are always present. Oftennoise and/or interference are the limiting factors in messagingreliability. Noise also affects throughput by forcing frequentretransmit requests, backoff delays, and dropped messages. With theopening of higher frequencies in 5G and future 6G systems, phase noiseis expected to be increasingly problematic. The rapid proliferation ofwireless users, including machine-type nodes in high-densityenvironments, is expected to make the interference problem exponentiallyworse. What is needed is means for mitigating noise and interferencewhile providing improved throughput and reliability suitable for thehigh multi-GHz frequency bands and the extremely high spatial density ofnodes in the next generation of wireless networking.

Various attempts have been made to address the above issues. An articleby Kim and Lee (Optics Communications 474 (2020) 126084) mentionsnon-square modulation tables in the context of optical fibercommunications. Their attempt includes various odd-bit-number modulationtables such as 512QAM, which encodes 9 bits, but is otherwise standard(and square). An article by Lee et al (IEEE Transaction on Broadcasting(2017) DOI: 10.1109/TBC.2016.2619583) mentions non-square modulation,but in the context of DOCSIS (Data Over Cable Service InterfaceSpecification), which is entirely different from 5G wireless RFmessaging. And similar to the Kim-Lee article, the article refers to512QAM and the like. A U.S. Pat. No. 10,158,451 to Arambepola et alpurports to provide a way to map bit streams to modulated symbols, andincludes mixtures of tables. Referring to QAM2048, the reference termsthe same “non-square”, but in fact it has square symmetry. In short,these all have square symmetry. Finally, a US Patent Application2016/0337081 to Jung et al is directed to figuring out an unknownmodulation table by looking at an incoming signal. All of thesereferences are deficient in providing the type of high-speedhigh-reliability wireless messaging necessary for today'scommunications.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY OF THE INVENTION

Responsive to the problems listed above, systems and methods presentedbelow can provide network operators with a range of modulation solutionsthat enable nuanced and fine-grained mitigation of message failuremodes. While prior-art modulation tables generally allow only large-stepresponses to transmission faults, such as dropping from 256QAM to 64QAMor 16QAM, asymmetric modulation tables according to present principlescan offer more precise adjustment of noise margins using modulationtables configured to mitigate specific failure modes, and thereby avoidcostly retransmission, signal corruption, and dropped message issues.Means (including AI means) for selecting among available modulationtables, adjusting or optimizing a table according to observed messagefault types, and predicting network performance according to a selectedmodulation table, are also detailed below.

In a first aspect, a method for modulating symbols of a wireless messagecomprises: modulating symbols of a wireless message using a modulationtable comprising an array of integer Nstates modulation states, theNstates including integer Namp amplitude levels and integer Nphase phaselevels, a particular one iNstates of the Nstates modulation states beingdefined by an amplitude level iNamp and a phase level jNphase;modulating each symbol of the wireless message according to thecorresponding amplitude level and phase level of the states of themodulation table; and transmitting the modulated symbols from a firstdevice to a second device, wherein the modulation table has one or moreproperties selected from the following group: the modulation table isconfigured to violate square symmetry; the modulation table isnon-square; Namp is not equal to Nphase; a number of rows or a number ofcolumns differs in the modulation table before and after a 90° rotationof the table; one or more of the states has been declared invalid andnot used for modulation; Nstates is an integer other than a power of 2;the amplitude levels or phase levels are spaced apart nonuniformly.

In a second aspect, a data structure is stored in a non-transitorycomputer readable medium, the data structure forming a modulation tablefor use in modulating symbols of a wireless message, the modulationtable including an array of Nstates modulation states, the Nstatesincluding Namp amplitude levels and Nphase phase levels, a particularone iNstates of the Nstates modulation states being defined by anamplitude level iNamp and a phase level iNphase, wherein the modulationtable has one or more properties selected from the following group: themodulation table is configured to violate square symmetry; themodulation table is non-square; Namp is not equal to Nphase; a number ofrows or a number of columns differs in the modulation table before andafter a 90° rotation of the table; Nstates is not a power of 2; theamplitude levels are spaced non-uniformly; the phase levels are spacednon-uniformly; at least one of the states is declared invalid and notused for modulation.

In a third aspect, a modulation table is for modulating symbols of awireless message, the modulation table comprising Namp amplitude levels,Nphase phase levels, and Nstates states, each state defined by one ofthe amplitude levels and one of the phase levels, respectively, wherein:Namp and Nphase are configured to cause a rate of adjacent-amplitudefaults to equal a rate of adjacent-phase faults: an adjacent-amplitudefault comprises a symbol modulated at a first amplitude level anddemodulated at a second amplitude level adjacent to the first amplitudelevel; an adjacent-phase fault comprises a symbol modulated at a firstphase level and demodulated at a second phase level adjacent to thefirst phase level; and equal means the same within a predetermined valuewhich is 10%, 25%, or 50% of each of the rates.

In a fourth aspect, a wireless network comprises a base station and aplurality of user nodes configured to transmit wireless messagesmodulated according to a modulation table comprising Nstate modulationstates, each state being modulated according to a particular amplitudelevel of Namp amplitude levels and a particular phase level of Nphasephase levels, wherein: the Nstates comprise a two-dimensional Namp byNphase array; the array has 180-degree rotational symmetry and does nothave 90-degree rotational symmetry; and the 180-degree rotationalsymmetry comprises an isomorphism after rotation of the array by 180degrees and the 90-degree rotational symmetry comprises an isomorphismafter rotation of the array by 90 degrees.

In a fifth aspect, a method for selecting a resultant modulation tablecomprises: receiving a first message and determining that the firstmessage is faulted; receiving a second message subsequent to the firstmessage; determining that the second message is not faulted, whereineach of the first and second messages includes a respective plurality ofsequential symbols modulated according to an initial modulation table;comparing corresponding symbols of the first and second messages;determining a modulation difference between a particular symbol of thefirst message and a corresponding symbol of the second message; andselecting, based at least in part on the modulation difference, theresultant modulation table.

In a sixth aspect, a method for selecting a new modulation table,different from a current modulation table, comprises: receiving afaulted message, the faulted message modulated using a currentmodulation table; receiving a non-faulted message; comparing each symbolof the faulted message with a corresponding symbol of the non-faultedmessage; determining at least one symbol of the faulted message thatdiffers from the corresponding symbol of the non-faulted message;determining whether the differing symbols are modulated according to:respective members of adjacent amplitude levels; respective members ofadjacent phase levels; respective members of a high-amplitude portion ofthe current modulation table; and respective members of a low-amplitudeportion of the current modulation table.

In a seventh aspect, a method for adjusting a particular amplitude levelin a modulation table having Namp amplitude levels and Nphase phaselevels and Nstates states, each state determined by one of the amplitudelevels and one of the phase levels respectively, comprises: varying theparticular amplitude level while keeping at least two other amplitudelevels unchanged.

In an eighth aspect, a wireless network comprises a base station and aplurality of user nodes configured to transmit and receive wirelessmessages comprising symbols modulated according to a current modulationtable, wherein the base station is configured to: detect a messagefailure; determine one or more symbol modulation faults in the failedmessage; determine a modulation fault type for each symbol modulationfault by comparing symbols of the failed message with symbols of acorresponding successful message; and select a new modulation tableaccording to the modulation fault type or types so detected.

In a ninth aspect, a method for predicting a network performanceparameter, the method comprises: using, in a computer, an artificialintelligence array comprising a plurality of input parameters, an outputparameter, and a plurality of intermediate functions, each intermediatefunction depending functionally on one or more of the input parameters,and wherein the output parameter depends functionally on theintermediate values; measuring a number Famp of adjacent-amplitudefaults involving adjacent amplitude levels of a modulation table, anumber Fphase of adjacent-phase faults involving adjacent phase levelsof the modulation table, and a number Fnonadjacent of non-adjacentfaults involving non-adjacent amplitude or phase levels of themodulation table; setting three of the input parameters according toFamp, Fphase and Fnonadjacent; setting at least one additional inputparameter according to the modulation table; predicting, with theartificial intelligence structure, a predicted output parameter;comparing the predicted output parameter with a measured networkperformance parameter; and if the predicted output parameter disagreeswith the measured network performance parameter, adjusting one or moreof the intermediate functions to cause the predicted output parameter toagree more closely with the measured network performance parameter.

In a tenth aspect, an artificial intelligence (AI) mathematicalstructure comprises: a plurality of input parameters comprising one ormore wireless message fault rates and one or more parameters of amodulation table; a plurality of intermediate functions configured toperform calculations based at least in part on the input parameters; andone or more output parameters functionally related to the intermediatefunctions, the output parameters comprising a network performanceparameter.

In an eleventh aspect, an algorithm is configured to take, as input, aparticular modulation table of a set of modulation tables, and a messagefailure rate, and to provide, as output, a predicted message throughput,a predicted message failure rate, a predicted average delay per message,or combinations thereof.

In a twelfth aspect, an algorithm for selecting a selected modulationtable, comprising: one or more input parameters comprising a messagefault rate; one or more output parameters comprising a predicted networkperformance parameter.

Systems and methods according to present principles provide improved andoptimized techniques and devices for high-speed communications. Incontrast to those of the prior art, systems and methods according topresent principles employ asymmetric or non-square modulation tables in5G wireless RF messaging, which are termed and defined in various waysdescribed below, and which provide for particularly enhanced high-speedcommunications. For example, in contrast to certain of the prior art,certain arrangements of systems and methods according to presentprinciples assume that the base station and the user node already agreeas to which modulation table is in use, and thus there is no need todetermine it from the received signal. There is, on the other hand, needfor selecting an appropriate modulation table, from available modulationtables, and for adjusting or optimizing tables, to mitigate observedfault modes. AI systems and methods presented herein can assist in suchselecting, adjusting, and optimizing.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

These and other embodiments are described in further detail withreference to the figures and accompanying detailed description asprovided below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic showing a square-symmetric modulation tableaccording to prior art.

FIG. 2 is a schematic showing a square-symmetric modulation tableincluding noise contours and certain fault conditions, according toprior art.

FIG. 3 is a schematic showing an exemplary embodiment of an asymmetricmodulation table in which the number of phase levels is different fromthe number of amplitude levels, according to some embodiments.

FIG. 4 is a schematic showing an exemplary embodiment of an asymmetricmodulation table in which the number of amplitude levels is an integerother than a power of two, according to some embodiments.

FIG. 5 is a schematic showing an exemplary embodiment of an asymmetricmodulation table with non-uniform amplitude level spacing, according tosome embodiments.

FIG. 6 is a schematic showing an exemplary embodiment of an asymmetricmodulation table in which some of the states are invalid, according tosome embodiments.

FIG. 7 is a schematic showing an exemplary embodiment of an asymmetricmodulation table having both non-uniform amplitude levels and invalidstates, according to some embodiments.

FIG. 8 is a flowchart showing an exemplary embodiment of a method foranalyzing faulted messages, according to some embodiments.

FIG. 9 is a schematic representation of exemplary symbols in faulted andunfaulted messages illustrating fault types, according to someembodiments.

FIG. 10 is a graphic indicating exemplary mitigation options formitigating certain message fault types, according to some embodiments.

FIG. 11 is a flowchart showing an exemplary embodiment of a method forselecting a modulation table based on analysis of faulted messages,according to some embodiments.

FIG. 12 is a schematic showing an exemplary embodiment of an asymmetricmodulation table with an adjustable amplitude level, according to someembodiments.

FIG. 13 is a flowchart showing an exemplary embodiment of a method foradjusting an adjustable level in a modulation table, according to someembodiments.

FIG. 14 is a flowchart showing an exemplary embodiment of a method forpredicting network performance, according to some embodiments.

FIG. 15 is a schematic showing elements of an exemplary embodiment of anAI structure, according to some embodiments.

FIG. 16 is a flowchart showing an exemplary embodiment of a method forselecting a particular modulation table, according to some embodiments.

FIG. 17 is a schematic showing an exemplary embodiment of a modulationtable with variable amplitude level spacings, according to someembodiments.

FIG. 18 is a flowchart showing an exemplary embodiment of a method foradjusting a variable amplitude level of a modulation table, according tosome embodiments.

Like reference numerals refer to like elements throughout.

DETAILED DESCRIPTION

Systems and methods are disclosed herein (the “systems” and “methods”,also occasionally termed “embodiments” or “arrangements”, generallyaccording to present principles) that can provide urgently neededprotocols to mitigate noise and interference in 5G wireless networkingby providing modulation options, particularly amplitude and phasemodulation tables, beyond those currently available. The systems andmethods also include means, including AI (artificial intelligence)means, for predicting network performance according to the modulationscheme in use, for selecting a modulation scheme according to messagefaults observed, and for optimizing the performance of particularmodulation schemes, based on message failure modes.

Widely used modulation options in 5G include 16QAM, 64QAM, 256QAM, and1024QAM wherein the indicated number is the number of distinctmodulation states in a modulation table, and QAM stands for quadratureamplitude modulation (that is, modulation of both phase and amplitude).Such modulation tables indicate how the amplitude and phase of awireless signal may be modulated to encode digital information. Thewireless message consists of sequential modulated “symbols”, each symbolbeing a period of uniformly modulated signal at a particular frequencyand time. For example, 16QAM has 16 distinct modulation states, formedby four amplitude levels and four phase levels being appliedsimultaneously to an RF (radio-frequency) wave. Likewise 256QAM has 16amplitude levels and 16 phase levels, which when combined form 256distinct modulation states, and 1024QAM with 32 phase and amplitudelevels forming 1024 distinct states. Symbolically, the number ofamplitude levels may be represented as Namp, the number of phase levelsis Nphase, and (Namp×Nphase)=Nstates is the number of phase-amplitudecombinations or states in the table, each of which is a modulationstate. More generally, the modulation table may be a data structure,such as an array stored in a computer readable medium, used formodulating symbols of a transmitted message. Each of the prior-artmodulation tables is “symmetric” or “square-symmetric”. A symmetricarray, as used herein, is a two-dimensional array that, when rotated by90 degrees, remains isomorphic (same shape) as the original. Moreover,each of these modulation tables has an equal number of amplitude levelsand phase levels (Namp=Nphase), the various amplitude levels are evenlyor uniformly spaced apart when specified in some units, the phase levelsare equally spaced apart, the number of distinct modulation states inthe table is equal to the product of the number of amplitude levelstimes the number of phase modulation levels, and each of the distinctstates is a valid amplitude-phase modulation state for modulating eachsymbol of the message. In addition, the number of levels (Namp orNphase) is a power of 2 and the number of states Nstates is a power of4. More specifically, the number of amplitude or phase levels is 2^(N),where N=2, 3, 4, 5 for 4, 8, 16, 32 levels respectively. The number ofvalid states is equal to the number of states which is 2^(2N)=4^(N)=16,64, 256, 1024 for N=2, 3, 4, 5 respectively. Each state can encode anumber of binary bits equal to the logarithm, in base 2, of the numberof states, or log₂(2^(2N))=2N. Thus the number of bits per symbol in asymmetrical modulation table is 4, 6, 8, 10 for N=2, 3, 4, 5respectively. Modulation tables with larger numbers of distinct statesare thus able to convey information faster than smaller modulationtables, by encoding more bits per symbol. However, larger tables areusually more susceptible to noise and interference, due to the reducedseparation between modulation states. The effect of noise may begreatest for the lowest amplitude modulation states since these aregenerally the states with the lowest SNR (signal-to-noise ratio).Network operators may be forced to use higher transmission power thanotherwise necessary, to overcome demodulation faults which may beconcentrated in the lowest-amplitude modulated signal states. Inaddition, phase noise becomes an increasing problem at high frequencies,especially at the multi-GHz frequencies planned for 5G and beyond. Noiseand interference may result in frequent transmission faults, leading tomessage failures with subsequent delays and retransmission attempts.Wireless networks may therefore be forced to use lower degrees ofmodulation to regain reliable message transmission at high frequencies.

Amplitude is generally specified in volts or microvolts, alternativelyin watts or microwatts or dB (decibels), and phase in degrees orradians. In 5G systems, the received amplitude levels are compared toDMRS (demodulation reference signal) symbols, or the like, which serveas amplitude calibrations. Phase measurements on the received signal arecompared to PTRS (phase tracking reference signal) messages, or thelike, that calibrate the phase. When discussing symmetry, it isconvenient to normalize the amplitude levels and phase levels of amodulation table, so that the plotted values are unitless and range from0 to 1. For example, if the allowed amplitude levels are {Vmin, V2, . .. Vn, . . . Vmax}, then the corresponding normalized amplitude value Anfor level number “n” may be An=(Vn−Vmin)/(Vmax−Vmin) which is unitlessand ranges from 0 to 1. Likewise the phase values may be {Dmin, D2, . .. Dn . . . Dmax} and the corresponding normalized phase values Pn may bePn=(Dn−Dmin)/(Dmax−Dmin). In summary, symmetric modulation tablesgenerally have the same number of amplitude levels and phase levels, andthe number of such levels is a power of 2, the levels are equallyspaced, and all states in the table are valid.

Disclosed herein are “asymmetric” modulation tables that can, in someimplementations, enable network operators to mitigate particular noiseproblems in real-time, according to some embodiments. An “asymmetric”array, as used herein, is an array that violates square symmetry in thatit is not isomorphic to its 90-degree rotation. For example, a“rectangular” array has 180-degree rotation symmetry, but not 90-degreerotation symmetry. Exemplary asymmetric modulation tables, discussedbelow, are characterized by at least one of: (1) the number of phaselevels is different from the number of amplitude levels; (2) the numberof amplitude levels is an integer other than a power of two; (3) thenumber of phase levels is an integer other than a power of two; (4) thenumber of states in the modulation table is an integer other than apower of two; (5) the spacing of the phase levels is non-uniform; (6)the spacing of the amplitude levels is non-uniform; (7) at least one ofthe modulation states in the table is invalid or unavailable formodulation; and (8) combinations thereof. The number of valid states isNvalid and the number of invalid states is Ninvalid. Since every stateis either valid or invalid, the total number of states equals the validplus invalid states: Nstates=Nvalid+Ninvalid. In some cases it isinformative to determine whether errors occur primarily in a lowamplitude region of the modulation table or a high amplitude region, oruniformly throughout the table. A dividing line between the low and highamplitude regions may be a midpoint among the amplitude levels, orelsewhere. In this case, every state is either a low amplitude or a highamplitude state. The number of low amplitude states Nlow plus the numberof high amplitude states Nhigh then equals the number of total states inthe table, Nlow+Nhigh=Nstates=Namp×Nphase.

An asymmetric modulation table, according to some embodiments, mayinclude a number of amplitude levels different from the number of phaselevels; the numbers of amplitude and phase levels can be any integer,including integers other than powers of 2; the amplitude level spacingcan be non-uniform and the phase level spacing can be non-uniform; andsome of the states of the table may be invalid (that is, not used formodulating symbols) whereas other states may be determined as “valid”and legal for modulation. Amplitude levels are “non-uniformly spaced” ifthe separations between the various amplitude levels differ by more thana predetermined limit, such as 1% or 5% or 10% or 25% or 50% of eachseparation, for example. Asymmetric or non-square-symmetric modulationtables can provide valuable options enabling network operators tomitigate amplitude noise or phase noise, or a combination, whilemaintaining a high message throughput, according to some embodiments.Use of an asymmetric modulation table according to present principlescan thereby enable wireless networks to avoid message faults due todemodulation errors, resulting in fewer message faults, higher messagethroughput, and enhanced user satisfaction. As used herein, the message“throughput” is the number of messages successfully transmitted andreceived in the network per unit time. A message failure rate is thenumber of failed messages per unit time. A message is “faulted” or“failed” if its symbols disagree with an embedded CRC (cyclic redundancycheck) code or equivalent error-check code, and is “successful”otherwise. A failed message generally contains at least one “faulted”symbol (or symbol modulation fault) which is a symbol that was modulatedwith one value but demodulated with a different value, usually due tonoise or interference.

The systems and methods further disclose methods for determining whethera network can reduce message faults by switching to a differentmodulation table according to fault types observed, and for selectingwhich table would be more effective, and for optimizing the performanceof that table. For example, a node (user or base station) may receive amessage, read an embedded CRC code or the like, compare to the receivedmessage symbols, and thereby determine that the message is faulted. Thenode may then request a retransmitted message, and if the retransmittedmessage is unfaulted, the node can compare the two versionssymbol-by-symbol. By determining which symbols are different, and howthey differ, the node can determine which types of faults occurred. Forexample, if symbols were altered in phase, causing them to switch to anadjacent phase state in the table (“adjacent-phase” faults), then thenetwork may choose to switch to another modulation table with greaterseparation between phase states. Modulation faults between adjacentamplitude levels (“adjacent-amplitude” faults) may indicate the need fora table with greater separation between amplitude levels. Ifadjacent-amplitude faults occur mainly in the lower (low-amplitude)portion of the table, then another table with greater spacing betweenamplitude levels at the low-amplitude end of the table may be needed.However, if faults appear similarly in the low-amplitude andhigh-amplitude regions of the table, then a different modulation tablewith greater separation of both amplitude and phase states may be abetter choice. If the faults are non-adjacent, that is, symboldistortion is larger than the level spacing, then the faults are likelynot due to noise, but rather from pulsatile interference that overwhelmsthe signal occasionally. In that case a table with greater separationbetween states is likely to be futile because the distortions are toolarge. Instead, a better strategy may be to switch to a table with evensmaller level separations, and therefore more modulation states, inorder to reduce the time required to transmit each message. Theshorter-duration message may thereby finish before the interferencestrikes again. By comparing faulted and unfaulted versions of the samemessage and determining specifically the modulation errors that causedthe fault, the node can select a more appropriate modulation table forreduced message failure rates or improved throughput, according to someembodiments.

Typically the base station (or core network) may detect and tally thetypes of message faults that it observes, and the user nodes may do thesame and periodically communicate their totals to the base station. Thebase station (or core network) can then process all that data along withother input such as the traffic density, the distribution of messagesizes, the distribution of frequent and infrequent users, the presenceand type of external noise or interference, the distribution ofpriorities among the messages, and many other parameters, in determininghow to optimize the modulation table. In addition, the base station mayassign a different modulation table to each user node, and mayindividually optimize each such table. Typically the base station caninform the user node or nodes of such a change in modulation table usingan RRC (radio resource control) message or a broadcast message or aunicast downlink message to the user node or nodes that are to switch tothe new modulation table. Thereafter, those user nodes may employ theselected new modulation table for modulating symbols of furthermessages, such as data messages uploaded to the base station on thePUSCH (physical uplink shared channel) or RACH (random access channel)or other suitable frequency.

In one embodiment, a base station or a user node can determine a messagefailure rate by counting the number of failed messages or the number offaulted symbols in the failed messages, per unit time, and then vary oneof the amplitude levels either up or down in value, and then measure themessage failure rate again. If the message failure rate goes down, theamplitude level can be varied further in the same direction, and if itgoes up, the variation can be reversed. This iterative process can becontinued until an optimal, or at least a satisfactory, setting has beenachieved.

More generally, a base station or core network may decide to change anetwork setting or procedure in use, responsive to a tally or analysisof the types of message failures observed. In contrast to prior-artnetwork management in which changes may be based on an undifferentiatedmessage failure rate, embodiments according to present principles caninstead determine which types of message failures occurred and underwhich circumstances, such as increased adjacent-amplitude andadjacent-phase faults at low amplitudes, among many other possible faulttypes. The network can then select a suitable change to mitigate theparticular types of faults most often detected, and thereby improve boththe message throughput and the failure rate, and usually the averagedelay time per message as well. Network management and decision-makingbased on the types of message failures encountered, rather than thegross failure rate, can thereby enable nuanced and problem-specificsolutions, leading to improved network performance and greater usersatisfaction.

The base station is typically very busy in 5G, and may have difficultyperforming the complex multi-dimensional optimization required.Therefore, means including AI means are disclosed for predicting futurenetwork performance according to the current operating conditions andthe types of message faults currently observed. With such predictions,networks may select which modulation table to use in particularcircumstances, and may also perform optimizations, such as optimizing anamplitude level to minimize faulting for example. Alternative AI meansare disclosed for preparing an algorithm that enables the base station(or core network) to predict network performance based on each availablemodulation table, and thereby select a suitable modulation table for thecurrent conditions. Algorithm means are also disclosed for adjustingvariables in a modulation table, such as an amplitude level, for furtherimprovements in network performance. With such an algorithm, the basestation or core network may switch to the better modulation table andthereby provide reduced message failures and an improved networkingexperience for the users overall.

Typically an optimization based on AI includes a mathematical AIstructure in a computer. The AI structure may include a plurality ofinput parameters, at least one output parameter, and a plurality ofintermediate functions (or “propagation” functions). The inputs,intermediate functions, and outputs are connected together by directed“links” representing the transfer of processed information between theseentities. In some embodiments, the AI structure may be a neural net(cascaded decision tree with adjustable interdependent functionalneurons), a hidden Markov model (array of nodes operationally dependentin a complex, usually unknown, manner), or other means for calculatingthe output parameter from the input parameters. For example, the inputparameters may be the number or rate of message failures of each type,such as Famp for adjacent-amplitude faults, Fphase for adjacent-phasefaults, and Fnonadjacent for non-adjacent type faults, and optionallyFlow and Fhigh for faulted states in the lower or higher amplituderegions of the table respectively, as well as other failure modes thatmay be detected. The input parameters preferably include at least oneparameter of the current modulation table, in use when those faults areobserved. The modulation table parameter(s) may include the number ofamplitude and phase levels, the number of states in the table,separations between the amplitude or phase levels, presence or absenceof invalid states, and the like. The output or outputs may includenetwork performance metrics such as the message throughput, the messagefailure rate, an average delay time, a dropped message rate, andcombinations thereof, as well as other measures of interest regardingthe performance of the network. The network performance may be acomposite performance metric, such as the message throughput minus tentimes the message failure rate, or some other measure of interest tonetwork operators. The intermediate functions are functions or computercodes that depend on the input parameters or other higher-levelintermediate functions. The intermediate functions may be linearfunctions such as weighted sums, or nonlinear functions, or othercombination of the parameters, and may also include logical combinationsof the parameters such as Boolean combinations. Often the intermediatefunctions are connected in layers, such that the first layer receivesdata from the input parameters and passes its processed results to thesecond layer, which further combines and processes the information, andthen finally the last layer feeds all of its processed data to theoutputs. Thus the output parameter(s) depend functionally on theintermediate functions, and indirectly on the inputs. In addition, eachdependency (or “link”) may also be weighted, and the weight may bevariable. In one example, each intermediate function may be a weighted(including negatively weighted) sum of the higher-layer intermediatefunction results. The AI structure may thereby embody a directed,weighted, acyclic or feed-forward, “graph” or tree. (Recursive-typegraphs, with backward links or sidelinks, may also be used, althoughconvergence is then not guaranteed.)

The intermediate functions generally include variable values that may be“tuned” or “trained”, meaning that the variables are adjusted so thatthe final output of the structure approaches measured data or otherdesired output. In the current application, the variable values may beadjusted to provide a prediction. For example, the output parameter maybe a prediction of a network performance metric. The values may beiteratively adjusted until the output parameter predicts the networkperformance metric to sufficient accuracy. For example, in a neural net,each intermediate function (called a “neuron” in analogy to brain cells)may include a number of weights and thresholds, and optionally logicelements, by which the input parameters or the results of previousintermediate functions may be combined. An example of a linearintermediate function may be the formula “v1 times theadjacent-amplitude fault rate plus v2 times the adjacent-phase faultrate minus v3 times the non-adjacent fault rate”, where v1, v2, and v3are adjustable “weights” or weighting coefficients applied to the inputlinks, and the output of the function is provided to the next layer orthe output layer. An example of a non-linear or logical intermediatefunction may be “select the larger of the adjacent-amplitude and v4times the adjacent-phase fault rates if either one is larger than v5times the non-adjacent fault rate, and select the square root of theinverse of the non-adjacent fault rate otherwise”. The values v1-v5 andso forth may be adjusted to optimize the predictive accuracy of the AIstructure. For example, the input parameters may be set according to themeasured network data, failure modes, modulation table in use, and soforth as described above, and the AI structure may then be used topredict the resulting network performance metric. More specifically, thevarious intermediate functions may be calculated according to the inputparameters, and the outputs (the predicted network performance metric inthis case) may be calculated according to the results of theintermediate functions. Usually, the predicted network performancemetric will differ from that measured. The intermediate functions may beadjusted or varied, and the calculations repeated to obtain an updatedprediction of the network performance metric. The adjustable values ofthe intermediate functions may then be varied at random or in a patternor according to a more purposeful method, to bring the predicted outputmore closely in agreement with the measured network performance metric.Successive predictions may be compared, to determine if the predictionaccuracy has been improved by the value changes and to guide futurevalue adjustments. The above process may be repeated for, preferably, awide range of network configurations, modulation tables, and fault typesobserved, thereby gradually improving the accuracy of the predictionsand broadening the range of conditions under which the predictions areaccurate. The adjustment of values may be performed on each recordedcase sequentially, or on groups or “batches” of similar scenarios withaveraged input parameters, or otherwise. However the training isperformed, the AI structure is preferably then tested using new datathat the AI structure has never before seen.

When the AI structure has been adjusted so that it produces sufficientlyaccurate predictions of network performance, the AI structure can thenbe passed to base stations or core networks (preferably with theintermediate function values frozen) so that the network can predict thenetwork performance according to various choices of the modulationtable, and thereby select which table to use. Alternatively, the AIcomputer or another system may prepare a predictive algorithm based onthe AI structure. For example, the AI structure may be simplified by“pruning” any input parameters or intermediate functions that exhibitlittle or no correlation between the modulation table and the predictedperformance. Based on the AI structure, the algorithm may be an analyticfunction, a computer code, a tabular or matrix array, or other means forderiving a network performance prediction from the input parameters. Foreven simpler implementation, the algorithm may be configured toautomatically monitor network conditions including faults, predictperformance metrics according to each available modulation table,determine which table is expected to provide the best performancemetric, and signal the network that the selected modulation table couldprovide improved performance. The network could then switch to theindicated modulation table without having to perform the analysisexplicitly.

As a further alternative, the AI structure may be configured to assistnetwork operators in adjusting the individual levels of a modulationtable, such as adjusting one or more amplitude levels, to mitigateadjacent-amplitude faults for example. To do so, the AI structure mayinclude, as inputs, values corresponding to the amplitude levels of amodulation table, and may vary one or more of those amplitude levels topredict the performance versus the amplitude level setting. This processmay be repeated by varying each amplitude level in the modulation table,and may continue in an iterative cycle until optimal or near-optimalsettings of the amplitude levels have been determined. The phase levelsmay be adjusted in the same way.

As a further alternative, the AI structure can be used to predictnetwork performance according to a wide range of operating conditionsother than modulation tables. For example, AI structures (or algorithmsderived from them) may assist network operators in resolving problemsand optimizing operations under conflicting demands. The AI structure oralgorithm may assist the network in determining which frequencies andbandwidths to assign to various user nodes, what power levels and beamconfigurations to employ while avoiding interfering with other usernodes or other networks, how much bandwidth and other resources todedicate exclusively to individual users (such as dedicated “SRopportunities” which are pre-assigned times at which certain user nodesmay exclusively transmit an SR scheduling request message), how toallocate resources among a multitude of user nodes having a range ofpriorities and message sizes in both uplink and downlink, and a maze ofother decisions regarding the network operations in fast-pacedmassively-parallel 5G and 6G networks. No human could handle the task,nor most computers, due to the demanding cadence and complexity.However, AI-developed algorithms, as taught herein, based on actualnetwork data and trained with a sufficiently wide range of conditions,are well-suited to this type of problem and may enable network operatorsto operate future networks with greater efficiency than achievableotherwise. Artisans may develop such network-based AI structures and/ortheir derived algorithms after reading the matter and examples presentedherein.

Turning now to the figures, FIG. 1 is a schematic of a modulation tableaccording to prior art. The modulation table is presented graphically,with amplitude modulation levels (A1-A4) plotted along the vertical axisand phase modulation levels (P1-P4) along the horizontal axis, and eachdistinct modulation state, specified by one of the amplitude levels andone of the phase levels respectively, is shown as a “+” sign 101. In thedepicted modulation table, there are four amplitude levels and fourphase levels, thereby defining sixteen distinct modulation states. Allof the modulation states are allowed or valid in this case, meaning thatthey can all be used for modulating symbols in a message. In addition,the amplitude levels A1-A4 are spaced apart uniformly by an amplitudespacing 104, and the phase levels P1-P4 are spaced apart uniformly by aphase spacing 103. In a particular case, the phase levels P1-P4 may be−135, −45, +45, and +135 degrees, which are equally spaced at 90 degreesapart. Likewise the amplitude levels A1-A4 may correspond to 0.4, 0.6,0.8, and 1.0 times a maximum permitted amplitude, for example, and arealso evenly or uniformly spaced apart. The depicted example correspondsto 16QAM, which is a “square-symmetrical” modulation table in that thenumber of phase levels equals the number of amplitude levels, the phaseand amplitude levels are uniformly spaced, and all 16 distinctmodulation states are valid. In addition, the number of amplitude levelsis a power of 2, as is the number of phase levels, the number of totalstates, and the number of valid states in the modulation table.

FIG. 2 shows a similar prior-art modulation table with four amplitudelevels A1-A4 and four phase levels P1-P4, with each modulation state 201marked with a “+” symbol. The amplitude spacing between amplitude levelsis 203 and the phase spacing between phase levels is 204. Also shown, asan oval 202, is a contour of the SNR (signal to noise ratio) in theamplitude direction and the phase direction of the received signals foreach modulation state. A noise-free signal would correspond to a pointat the “+” sign 201 only, but in general the received signals aredistorted by electronic noise in the transmitter and receiver,attenuation of the signal in propagation, variations in the gain ortime-base or other parameter of the transmitter, the transmitterantenna, the receiver, the receiver antenna, and other random orpseudorandom variations, all of which will be included comprehensivelyas “noise” herein. In addition, occasional external “interference” suchas signals from other transmitters can appear, further degrading themessage quality when it occurs.

Also shown is the phase width 208 and amplitude width 207 of the noisecontour 202, indicating the phase noise and the amplitude noiserespectively. Noise fluctuations and interference can cause excursionsoutside the plotted noise contour 202, in which case the receiver wouldlikely interpret the modulation state 201 incorrectly, causing a messagefault. Also shown is a “gap” between the noise contours 202 of adjacentstates 201, such as an amplitude noise gap 205 and a phase noise gap206. The amplitude gap 205 is a symbolic representation of the amplitudenoise margin, which is a measure of how much the amplitude noise mayincrease without causing the adjacent amplitude levels to overlap, andlikewise the phase gap 206 is a graphical indication of the phasemargin, in this graphical schematic. Mathematically, the amplitude noisegap between two adjacent amplitude levels is equal to the separationbetween those two levels minus the average of the noise widths of thosetwo adjacent amplitude levels, and likewise for phase noise gaps. Aslong as the received signals remain within the indicated widths 207-208,the receiver is likely to detect and demodulate each symbol in themessage correctly, without fault. However, if the noise were to increasein either the amplitude or phase directions, such that the adjacentnoise distributions 202 begin to overlap, then the likelihood of amessage fault is increased. A fault occurs whenever a received symbol isinterpreted (demodulated) as a different value than transmitted. Thereceiving entity can generally determine that a fault has occurred bycalculating an error-check code, such as a CRC or other error-checkcode, based on the as-received symbols of the message, and thencomparing that value to a corresponding code embedded in the message. Adisagreement between the calculated value and the message code indicatesthat at least one symbol of the message was altered.

Also indicated in the figure are four types of faults by dashed arrows.The arrow labeled 221 is an “adjacent-phase” fault in which noiseoutside the normal phase width 208 caused a phase shift in a symbol. Thefaulted symbol was originally modulated in the A1-P2 state (that is, theamplitude level of the modulation was the A1 level and the phase wasP2). The phase noise distorted the symbol so that it appeared to havethe A1-P3 modulation instead. Since those two modulation states areadjacent in the modulation table, the fault is termed an“adjacent-phase” fault. (Adjacent-phase faults also include faultsinterchanging the P1 and P4 phase levels, since phase is a cyclicparameter.) An adjacent-amplitude fault is indicated by arrow 222, inwhich amplitude noise caused an A1-P2 modulated symbol to acquire extraapparent amplitude, bringing it into the adjacent state of A2-P2. Dashedarrow number 223, terminating in a small star, is an ambiguity-statefault in which the A1-P2 state was distorted by noise into a region ofthe modulation table that is not a legal modulation for any of themodulation states. Dashed arrow number 224 shows a non-adjacent fault inwhich a larger distortion, such as external interference, changed theA1-P2 modulated symbol to look like a distant modulation state of A4-P4.Non-adjacent faults also include distortions that drive the symbolentirely off the table. Each of these faults would cause the message tofail an error-check code, thereby revealing the message failure.

FIG. 3 is a schematic of an exemplary embodiment of an asymmetricalmodulation table according to present principles, in which the number ofamplitude levels is different from the number of phase levels, accordingto some embodiments. The depicted modulation table has 8 amplitudelevels A1-A8 and 2 phase levels P1 and P2, thereby providing 16 distinctmodulation states. This is the same number of states as the 16QAMexample of FIG. 1, but now distributed in an 8×2 configuration (that is,Namp×Nphase, or 8 amplitude levels×2 phase levels). The example thusdepicts a modulation table in which the number of phase levels isdifferent from the number of amplitude levels. In one embodiment, theamplitude levels A1-A8 correspond to 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,and 1.0 times a maximum permitted amplitude, and thus in this case areequally spaced with a spacing 303 of 0.1 times the maximum amplitude.The two phase levels P1 and P2 correspond to −90 and +90 degrees, whichare also equally spaced 304 in the sense that the phase levels areseparated by 180 degrees. Each distinct modulation state is indicated bya “+” 301 surrounded by a noise contour 302. Also shown is the amplitudegap 305 and the phase gap 306 separating the adjacent noise distributioncontours 302. The amplitude levels are more closely spaced here than forthe example of FIG. 2, and hence the amplitude gap 305 is relativelysmaller (other things being equal). The phase levels are more widelyspaced than in FIG. 2 since there are now only two phase levels, andhence the phase gap 306 is relatively larger.

An advantage of the modulation table of FIG. 3 can be improved phasenoise immunity. If a network, using a symmetrical modulation table suchas that of FIG. 2, detects more phase errors than amplitude errors, thenetwork may wish to change to the asymmetrical table of FIG. 3 in orderto increase the phase gap and hence reduce the incidence of phasemodulation errors. Such a strategy may be especially valuable at highfrequencies, where phase noise usually causes more faults than amplitudenoise. Thus the asymmetrical modulation table of FIG. 3 can provideimproved phase margin, with only minimal loss of amplitude resolution,and hence a net reduction in message failure rates, according to someembodiments.

FIG. 4 is a schematic of an exemplary embodiment of an asymmetricalmodulation table in which the numbers of amplitude levels or phaselevels is/are not equal to a power of 2, according to some embodiments.In the depicted table, there are three phase levels and six amplitudelevels; hence, Nstates=Namp×Nphase=6×3=18. Neither Namp nor Nphase is aninteger power of 2. The amplitude levels A1-A6 in this example may beuniformly spaced at 0.25, 0.4, 0.55, 0.7, 0.85, and 1.0 times thepermitted maximum amplitude, respectively. The phase levels P1-P3 mayalso be uniformly spaced at −120, zero, and +120 degrees relative to acarrier (or alternatively at −60, +60, and +180 degrees, if desired, toavoid using the carrier phase as a modulation level). As before, thevalid states 401 are indicated by a “+” and the noise distribution by anoval 402.

An advantage of the depicted modulation table may be, in someimplementations, that the amplitude noise width 405 is equal to theamplitude gap 407, and the phase noise width 408 equals the phase gap406. Thus, in this case, the ratio of the noise width divided by thegap, is the same for both amplitude and phase modulation. In some cases,lower message failure rates may be obtained by use of a modulation tablethat provides similar noise margins, or similar gap-to-width ratios, forthe amplitude and phase levels, as indicated here. In anotherembodiment, a ratio may be determined for non-uniform level spacings, inwhich the ratio equals the gap between a pair of adjacent amplitudenoise contours divided by the average of the two adjacent noise widths.The levels may be configured to make this ratio equal for all theamplitude levels, and likewise for the phase levels. With the gaps thusadjusted according to the noise widths, the overall fault rate can bereduced, according to some embodiments.

Another advantage of the modulation table of FIG. 4 may be that the 18states, being 2 more than the usual 16QAM states, can provide additionalcommunication flexibility. For example, all 18 states may be used formodulating symbols of wireless messages, resulting in fastertransmission than a standard 16QAM table as shown in FIG. 1.Alternatively, the “extra” states beyond the standard 16 could indicatea change in the modulation table to be used for demodulating the rest ofthe message, one of the extra states indicting a switch to analternative table, and the other extra state indicating a switch back tothe original table. Alternatively, 16 of the states may be used formodulating symbols of messages corresponding to the “regular” 16modulation states of a symmetric modulation table such as 16QAM, and the“extra” two states may be put to service carrying other information,such as a parity or error-checking code, or a QoS (quality of service)requirement, or an emergency indicator, or a sequence value, or otherflag or parameter beyond the actual message data. In one embodiment, 16of the modulation states may be “regular” states, meaning that they areused for modulation of the message symbols, while the two “extra” statesare used for some other purpose, other than modulating the symbols ofthe message. The 16 regular states may be assigned to correspond to aprior-art modulation table such as the 16 states of 16QAM. The 17thstate may be used to indicate the beginning of a message, and the 18thstate may be used to indicate the ending of the message. Use of theextra modulation states for indicating something about the message otherthan the actual data of the message, such as the beginning and ending ofthe message, may simplify the detection and decoding of certain messagessuch as unscheduled messages on, for example, the random access channel.The extra symbols, used in this way, may avoid time-consuming messagerequirements such as multi-symbol preamble requirements and the like, byunambiguously showing the starting and ending of the message content.The message content consists of all the symbols between the two “extra”symbols. The extra symbols are then recognizable as such since they aremodulated according to the 17th and 18th states. Artisans may findadditional valuable uses for symbols modulated according to the extrastates provided by an asymmetric modulation table in view of theteaching disclosed herein.

FIG. 5 is a schematic of an exemplary embodiment of an asymmetricalmodulation table with non-uniformly spaced amplitude levels, accordingto some embodiments. The modulation states are indicated by a “+” 501and the oval 502 indicates a contour of the SNR, signal to noise ratio.The amount of amplitude noise is relatively constant throughout thetable, but the signal strength is much lower at the low-amplitude end ofthe table (A1) than at the high-amplitude end (A4), and therefore thecontours 502 show a steady decrease in width, in the amplitudedirection, as the signal amplitude is increased. In this example ofnon-uniformly spaced amplitude levels, the amplitude levels A1-A4 maycorrespond to 0.3, 0.7, 0.9, and 1.0 times a permitted transmissionamplitude. Thus the amplitude levels are spaced apart by a relativelylarger space 503 at the low amplitude end of the table, and by arelatively smaller space 513 at the high amplitude end of the table. Thephase levels P1-P4 are uniformly spaced at −135, −45, +45, and +135degrees relative to a carrier. The amplitude noise width at the A1 levelis shown as 505, and the gap between the noise contours of the A1 and A2levels is shown as 507. Likewise the much smaller amplitude noise widthof the A4 level is shown as 515 and the gap between the A3 and A4 levelsis shown as 517. As can be seen in the figure, the A1-A2 gap 507 isequal to, or at least similar to, the A3-A4 gap 517, even though theamplitude noise widths 505 and 515 are quite different. For example, theamplitude noise gaps 507-517 may be made equal to within 1%, 5%, 10%,25%, or 50%.

An advantage of spacing the amplitude levels A1-A4 non-uniformly may bethat the rate of message failures may be reduced, according to someembodiments. For example, one or more amplitude level may be adjusted tomitigate a low SNR at low amplitude levels. As shown, the amplitudelevel spacing may be adjusted according to the measured noise widths ofthe various amplitude levels. The adjusted amplitude levels may therebyprovide similar gaps, or other measure of noise margin, betweenamplitude levels throughout the table, which may result in fewer faults.Alternatively, the amplitude levels may be arranged to provide similarvalues of the gap-to-width ratio, or other measure of reliabilitydepending on what produces the best network operation. In a practicalnetwork, the amplitude levels may be adjusted empirically to minimizethe number of message faults due to amplitude modulationmisidentification, for example. Each user node may employ its ownmodulation table, with an adjusted set of amplitude levels, differentfrom those of the other user nodes, to combat location-dependent noiseand device-dependent noise, for example. In addition, the base stationmay use yet another, different set of amplitude levels for downlinkmessages to each of the user nodes, for example to provide optimalreception to each user node by compensating the particular attenuationor noise factors experienced by each of the user nodes.

FIG. 6 is a schematic of an exemplary embodiment of an asymmetricmodulation table that includes invalid states, according to someembodiments. As mentioned, each modulation state in the table is definedby one of the amplitude levels A1-A4 and one of the phase levels P1-P4,respectively. Valid states are indicated by a “+” 601 and invalid statesare left blank, except for one invalid state 611 which is indicated by adashed circle. The noise distribution is indicated by a circle 602 inthis case, of width 605, indicating that the noise is similar in theamplitude and phase directions. The four amplitude levels A1-A4 areseparated by a uniform separation 603, and the four phase levels P1-P4are also uniformly spaced by spacing 604. As can be seen, the valid andinvalid states are alternated or interleaved in a checkerboard-likefashion, so that each valid state is surrounded on four sides by invalidstates, and each invalid state is surrounded on four sides by validstates (except for the edges). Stated differently, each valid state isadjacent, or next to, four of the invalid states and each invalid stateis adjacent to four of the valid states, the adjacency being bothrow-wise and column-wise. However, this only applies to “interior”states, which are states not in the first and last rows and columns ofthe table which represent the edges of the table. Each valid state maythen be surrounded on four sides by either invalid states or by the endsof the table, and each invalid state may be surrounded on four sides byeither valid states or by the ends of the table.

An advantage of providing both invalid and valid states in themodulation table, in this case an equal number of valid and invalidstates, may be that the noise margin of the remaining valid states maybe increased thereby, in both amplitude and phase directions. Forexample, the amplitude gap between the noise distributions of validstates, indicated as 607, is much larger than it would have been if allthe states were valid. The phase gaps are similarly increased relativeto a fully-occupied table. The incidence of message failures due toadjacent-amplitude and adjacent-phase faults may be reduced due to theincreased gaps between neighboring valid states. The nearest validneighbor to each valid state is diagonally positioned, hence bothamplitude and phase would have to be misidentified to cause a “diagonalfault” spanning the diagonal gap 610. For these reasons, a networkoperator may select such an asymmetric modulation table to reducemessage failure rates. The data rate is reduced slightly since eachsymbol can then carry one less bit of information when only half of thepossible modulation states are valid. For example, a 256QAM tableencodes 8 bits per symbol, and if half the states are made invalid, themodified table then encodes just 7 bits per symbol, resulting in a 15%reduction in data rate. However, the network operator may compensate forthat reduction by increasing the transmission rate (using higherfrequencies or higher bandwidths for example). Such higher frequenciesor bandwidths may not be feasible with prior-art symmetric modulationtables in which all the states are valid, due to their smaller noisemargins. But by switching to the depicted asymmetric table, with itslarger separation between valid states, the network operator may be ableto obtain higher transmission rates without significantly increasing themessage failure rate. In other words, the network may obtain increasednoise immunity using an asymmetric modulation table in which alternatestates are invalid, and as a consequence may increase the throughputusing faster transmission while maintaining lower failure rates, therebyobtaining a net win in overall performance, according to someembodiments.

FIG. 7 is a schematic of an exemplary embodiment of an asymmetricmodulation table with non-uniform level spacing and with strategicallyselected invalid states, according to some embodiments. Amplitude levelsA1-A4 are non-uniformly spaced, starting with spacing 703 at thelow-amplitude end of the table, and decreasing to 713 at thehigh-amplitude end of the table. The amplitude noise width ranges from alarge value 705 at the low-amplitude end of the table, to a much smallervalue 715 at the high-amplitude end. The non-uniform spacing of theamplitude levels is intended to compensate for a reduced noise marginfor the lower amplitude signals due to the larger noise width (or thereduced SNR) at low amplitudes. However, in this case the phase noise isalso large at low amplitudes, which may be a result of the increaseddifficulty for the receiving electronics to resolve the phase of asignal at low amplitude, since the SNR is lower than for a signal with ahigher amplitude. The phase noise problem is not helped by making theamplitude level spacing non-uniform. One possibility is to declare halfof the states to be invalid, as in FIG. 6, but that would eliminateseveral valuable transmission states in the high-amplitude end of thetable. That may not be desirable because the best signal transmission isobtained at the high amplitude end of the table, due to the higher SNRthere. A better way to mitigate the phase noise degradation at lowamplitudes may be to make certain states invalid, preferably in analternating manner, at the low-amplitude end of the table only. In thedepicted case, alternate states are made invalid in the amplitude levelsA1 and A2 only, whereas all the high-amplitude states at levels A3 andA4 remain valid. By strategically declaring certain modulation states,characterized by low signal or high noise, to be invalid states, whilepreserving the remaining states as valid, the network can improvereliability due to increased noise margin among low-amplitude states,with only slight loss of transmission rate due to the small number ofinvalidated states, according to some embodiments.

An advantage of making alternate states invalid in the region of themodulation table where the noise margin is poor, but keeping all thestates valid in other areas of the table where the noise margin issatisfactory, may be to reduce message failures with minimal reductionin transmission rate. For example, the message rate using the modulationtable of FIG. 7 corresponds to 12 valid states, compared with 16 validstates in FIG. 5, a reduction of only 25% in the number of availablemodulation states or a fraction of a bit per symbol in informationdensity. This may be compensated by the time saved in avoidingretransmissions of faulted messages, and further compensated by possiblyincreasing the frequency or bandwidth or other parameter to optimize thethroughput overall.

Asymmetric modulation tables may be designated by names indicatingfeatures of the modulation table. For example, a modulation table withNamp different from Nphase may by designated by listing the numbers ofamplitude and phase levels separated by “×”, such as 8×2QAM for thetable of FIG. 3 with Namp=8 and Nphase=2, or 6×3QAM for the table ofFIG. 4 with Namp=6 and Nphase=3. A table with non-uniform spacingbetween levels may be designated by “n” between the amplitude and phaselevel numbers, such as 4n4QAM for the table of FIG. 5. A table withinvalid states may be designated by an “i” after the numbers, such as16iQAM for the table of FIG. 8. If the table has invalid states as wellas a non-uniform level spacing, such as that of FIG. 7, the table may bedesignated as 4n4iQAM. A table with a variable level, such as that ofFIG. 12 below, may be designated with a “v” as in 4v4QAM. With such acompact but versatile informative designation, various types ofasymmetric modulation tables may be conveniently categorized.

FIG. 8 is a flowchart showing an exemplary embodiment of a method forselecting a modulation table based on the observed fault types,according to some embodiments. An intent of this method is to determinemessage failure modes from the faulted messages, and then select whichmodulation table would likely reduce those failure modes. In thisexample, ambiguous-state faults, which fall outside the normal noisecontours of the valid states (such as arrow 223 in FIG. 2), are countedin the total number of nonadjacent faults. Initially at 801, a node(base station or user node) receives a wireless message and checks at802 whether the message is faulted. For example, faults may be detectedby comparing the as-received message with an embedded error-check code,or by detecting at least one illegal modulation such as a modulationbeyond the edges of the table, in the message. If the message wasunfaulted, that is, successfully received and matching the error-checkcode, then flow returns to 801 to receive another message. If themessage is faulted, then at 803 a retransmission is requested using, forexample, the HARQ (hybrid automatic repeat request) procedure ifimplemented, or other retransmission message request as appropriate. Ifthe retransmitted message is successfully received at 804, then at 805the node compares the faulted and successful messages, symbol-by-symbol,to determine which symbols were altered and how they were altered. At806, a tally of adjacent-amplitude faults and a tally of adjacent-phasefaults are counted, as well as a total number of faults in a period oftime. An adjacent-amplitude fault is a received symbol that differs fromthe corresponding symbol in the unfaulted message by one amplitudemodulation level, and an adjacent-phase fault is a symbol that differsfrom the correct symbol by one phase modulation level.

At 807, the node determines the distribution of faults occurring invarious regions of the modulation table. For example, fault totals maybe tallied for each valid state of the table, and separate totals may betallied for faults occurring in the low-amplitude and high-amplitudeportions of the table, among other patterns. At 808, the node selects aparticular modulation table that can reduce the incidence of faults thathave been detected, and at 809 the node sends a message (to the basestation if a user node, or to the user nodes if a base station)recommending that the current modulation table be changed to theselected one. That message is sent using the current modulation table,of course, since the change has not yet been implemented. There may beother messages involved, such as a beacon message or RRC (radio resourcecontrol) message or the like, transmitted by the base station,indicating a change in modulation tables. Thereafter, at 810, the nodebegins using the selected modulation table for future messages such asdata messages transmitted on a PUSCH (physical uplink shared channel) orother channel.

An advantage of performing such a fault analysis on the as-receivedmessages (by comparing the corresponding symbols of faulted andunfaulted messages) may be that the fault information may reveal that amore effective modulation table is available, and that switching to thattable may reduce future faults of the types most often encountered.Thus, the depicted method may be an efficient way to determineobjectively which modulation table is able to provide improved networkperformance, according to some embodiments.

FIG. 9 is a schematic showing an exemplary embodiment of two wirelessmessages, according to some embodiments. One of the messages 901includes faults and the other 902 is without fault, as labeled. Eachmessage is a series of symbols modulated according to an amplitude leveland a phase level, indicated for example as A1-P3 for the amplitudemodulation level A1 and the phase modulation level P3, referring to aparticular modulation table such as that of FIG. 5. Faults can bedetected by comparing the modulation state of the corresponding symbolsfor the two messages and noting which symbols are different. In thefigure, symbols that differ between the two messages 901-902 are shownbolded and connected by a dashed line. The first three symbols of thetwo messages 901-902 are identical, but the fourth symbol is different.This is an example of an adjacent-amplitude fault 903 because thedisagreement between the two corresponding symbols is a single amplitudelevel, specifically A4 in the unfaulted message 902 has been changed toA3 in the faulted message 901, a change of one amplitude level. Alsoshown is an adjacent-phase fault 904 with the phase changed from P1 toP2, a change of one phase level. A non-adjacent fault 905 is also shown,in which the amplitude modulation level was changed from A4 to A1, achange of more than one amplitude level. It may be noted that a changein phase modulation from P4 to P1 would count as an adjacent-phasefault, because the phase dimension is circular.

In a wireless network, a base station or a core network may beconfigured to detect faulted and unfaulted messages according toagreement with an embedded CRC code or equivalent error-check code, andthen may compare the corresponding symbols of the two messages to locatethe faulted symbols and determine, from the size of the amplitude orphase modulation disagreement between the two messages, whether thefaults are adjacent-amplitude or adjacent-phase or non-adjacent typefaults. Since noise often causes near-neighbor, or adjacent type faults,whereas non-adjacent faults may indicate larger but sporadicinterference or the like, different mitigations may be needed in thosecases.

FIG. 10 is a schematic showing an exemplary relationship between faulttypes detected and options for mitigation of those faults. To mitigateadjacent-amplitude faults, the modulation table may be changed to onewith fewer amplitude levels, and therefore a wider spacing betweenamplitude levels, thereby increasing the noise margin for amplitudedistortion noise. To mitigate adjacent-phase faults, a differentmodulation table with fewer, and more widely spaced, phase levels may beadvantageous. If both adjacent-amplitude and adjacent-phase faults aredetected, the modulation table may be altered by making alternate statesinvalid for modulation in a portion of the modulation table or in theentire modulation table, depending on whether the problem is localizedto a portion of the modulation table or is seen roughly equallythroughout the table. Making alternate states invalid thereby increasesthe amplitude and phase separation between the remaining valid state,resulting in increased noise margin and likely fewer faults.

If the faults are largely non-adjacent, then they may be caused byoccasional external interference, in which case it may be beneficial tomake the messages chronologically shorter to sidestep the interferenceif possible. To do so, the modulation table may be changed to a largerone with more amplitude and phase levels, thereby encoding more bits persymbol, and therefore shortening the duration of each messageproportionally. Although the separation between amplitude and phaselevels would be made smaller by that change, this could result in onlyinsignificantly increased failures as long as the remaining noise marginis still sufficient. The network may have to test this by running thelarger modulation table and checking whether the total failure rateincreased or decreased.

In some cases, the adjacent-type faults may be concentrated in oneportion of the modulation table, such as the lowest amplitude levelswhere the SNR may not be as large as for the high-amplitude levels. Inthat case, the amplitude levels in the low-amplitude portion of thetable may be spread farther apart, with possibly the high-amplitudelevels being pushed closer together if the total range of amplitudemodulation remains constant. Likewise, if the faults are mainly in thehigh-amplitude levels, those levels may be spread farther apart, at theexpense of the low-amplitude level spacing. Whether this change reducesnet failures depends in a complex way on the noise properties, andtherefore the network would likely have to try such a modulation leveladjustment to determine whether it is successful. If so, the levels maybe adjusted farther in the same direction to determine whether theimprovement is increased. If, however, the adjustment does not enhancereliability, or makes it worse, the network can switch back to theoriginal modulation table.

In some cases, adjacent type faults may be detected throughout thetable, and for amplitude and phase faults equally. In that case, thenetwork may change to a modulation table in which alternate states aremade invalid, which greatly increases the noise margin of the remainingvalid states in most cases. If, however, that does not resolve theproblem, as a last resort the network may elect to increase thepermitted transmission power of the user nodes that exhibit excessivefaulting, or increase the downlink power to those user nodes, to enhancereception SNR and thus message reliability. Networks generally do notlike to increase the permissible power levels due to potentialinterference with other networks as well as the increased power demandsto which battery-operated user nodes may be sensitive.

FIG. 11 is a flowchart showing an exemplary embodiment of a method forselecting a particular modulation table based on detecting and analyzingmessage faults, according to some embodiments. The method of FIG. 11may, for example, implement the “selecting” of item 808 in FIG. 8. At1101, a node counts the number or rate of detections of various types ofmessage faults, such as the faults described in FIG. 8. The node mayaccumulate tallies of observed fault types for a period of time, eachtally indicating a number or incidence rate of each fault type. Forexample, the node could count the total number of faulted symbolsFtotal, the number of adjacent-amplitude faults Famp, the number ofadjacent-phase faults Fphase, the number of faults involving symbolsthat are not adjacent in the modulation table Fnonadjacent, the numberof faults occurring in the low-amplitude portion of the table Flow, andthe number in the high-amplitude portion Fhigh. At 1102, the node maycompare the number of adjacent to nonadjacent faults, specificallydetermining if Fnonadjacent is greater than the sum of the Famp plusFphase. If most faults are indeed nonadjacent, then the node mayconclude that the faults are due to occasional, large magnitudeinterference rather than random noise, since random noise usually causesadjacent modulation faults. Therefore, at 1103, the node may recommendusing faster transmissions in an attempt to sidestep the interference,using for example a larger modulation table (with more bits per symboland therefore fewer symbols per message and therefore a quicker message)as well as a higher frequency or bandwidth, among other steps to reducethe on-air time per message.

If, however, the adjacent faults exceed the nonadjacent faults at 1102,the node can then at 1104 determine whether the faults occurred mainlyin the higher or lower amplitude portions of the table. If the faultsusually occur in the low-amplitude portion, then at 905 the node maydetermine that the problem is the low signal strength in low-amplitudemodulated symbols, and may then ask whether the faults are mainlyadjacent-amplitude or adjacent-phase faults at 1105. If they are mainlyadjacent-phase faults, the node may recommend removing alternate statesat the low end of the table, as illustrated in FIG. 7, to providegreater phase margin as well as amplitude margin for low-amplitudesignals. If, however, the faults are mainly adjacent-amplitude typefaults in the low-amplitude portion, the node may suggest at 1106 thatthe amplitude levels be adjusted to provide greater spacing betweenamplitude levels at the low-amplitude end, and perhaps use a smallerspacing at the high-amplitude end of the table, to provide additionalneeded amplitude margin. For example, a particular amplitude level maybe adjusted in a first direction (that is, its value may be increased ordecreased) while the other amplitude levels are held constant, and theresulting change in a network performance metric, such as throughput ormessage failure rate, may be measured. Then, if the metric was improved(such as higher throughput or lower failure rate), the particularamplitude level may be adjusted in the same direction (increased ordecreased) as before, and the network metric may be again measured.Alternatively, if the result of adjusting the particular amplitude levelwas to make the network metric worse, then the particular amplitudelevel may be re-adjusted in the opposite direction (that is, decreasedif the initial variation was an increase, and vice-versa). In this way,by iteratively varying the particular amplitude level, or otheramplitude levels, while monitoring the network performance, improvedperformance may be obtained.

Returning to 1104, if the number of faults is not concentrated in thelow-amplitude portion of the table, then the node may determine at 1108whether the faults are mainly adjacent-amplitude or adjacent-phase typefaults. If they are mainly adjacent-phase faults, then at 1110 the nodemay suggest that a differently-shaped modulation table may be better,such as one with fewer phase levels, to provide greater phase margin.Likewise the node may determine, from the number of adjacent-amplitudefaults, whether the number of amplitude levels should be increased,decreased, or remain unchanged relative to the current table. If,however, the faults are mainly adjacent-phase faults at 1108, then at1109 the node may suggest a modulation table with fewer amplitude levelsand greater amplitude gaps to improve the amplitude noise margin. Themodulation table may be adjusted in this way until theadjacent-amplitude fault rate is approximately equal to theadjacent-phase fault rate, such as within 10% or 25% or 50% of eachother. After making the determination and recommending a change ofmodulation table at 1103, 1106, 1107, 1109, or 1110, the node is done at1111 until further faults are detected.

As an alternative, the node may find that the number of faults in thelower portion of the table roughly equals the number in the higherportion, and that most faults are adjacent-type faults, and that thenumber of adjacent-amplitude faults is roughly equal to the number ofadjacent-phase faults. In that case, the node may suggest switching to amodulation table with the same number and spacing of amplitude levels asthe current table, and the same number and spacing of phase levels asthe current table, but with alternate states made invalid, in acheckerboard-like pattern. This may increase the noise margin betweenadjacent states in both the amplitude direction and the phase direction,at a cost of only one bit per symbol in information density. Theincreased noise margins may result in fewer faulted messages and fewerretransmissions, among other benefits, thereby compensating for thereduced number of bits per symbol. In addition, the increased phasemargins can allow operation at a higher frequency or bandwidth or otherparameter, further compensating the reduced number of modulation states.

FIG. 12 is a schematic showing an exemplary modulation table in which anamplitude level is adjustable, according to some embodiments. Modulationstates 1201 are surrounded by noise contours 1202. The amplitudemodulation levels are shown on the vertical axis and the phase levels onthe horizontal axis. Several different settings 1221 of the secondamplitude level A2 are shown, indicating how the A2 level may beadjusted to mitigate, for example, adjacent-amplitude faults in thelow-amplitude portion of the table. The adjusted states corresponding toeach adjusted level are shown as further “+” signs 1222.

A network may adjust the A2 level by varying the amplitude setting amongthe candidate settings shown 1221 and monitoring the resulting messagefailure rate, especially the adjacent-amplitude fault rate in the lowerportion of the table. After testing several of the candidate settings1221 in this way, the network can select whichever candidate setting1221 provides the lowest failure rate, and can use that settingthereafter for improved message reliability.

FIG. 13 is a flowchart showing an exemplary embodiment of a method foradjusting an amplitude level in a modulation chart, according to someembodiments. One intent of this method may be to adjust the value of oneof the amplitude levels, such as the A2 level in FIG. 12, to minimizeadjacent-amplitude faulting in a wireless network. At 1301, the A2 levelis set to a midpoint of a predetermined range, or other starting value,and the fault rate, such as the adjacent-amplitude fault rate, is thenmeasured. Then at 1302 the A2 setting is incremented, or increased by anincrement, preferably a small amount, and again the fault rate isdetermined. At 1303, the change in fault rate is determined and, if thefault rate decreased, the flow returns to 1302 to continue varying theA2 setting in the same direction as before. If, however, the fault rateincreased at 1303, then at 1304 the step size, or amount by which the A2setting is varied, is decreased, such as being ¼ or 1/10 as large is theprevious increments, and at 1305 the A2 setting is then decremented ordecreased by the reduced step size. Again the fault rate is determinedat 1306, and if the fault rate then went down, the flow returns to 1305to continue decrementing. But if the rate went up at 1306, then at 1307the final decrement is reversed and the modulation table, with the A2level so adjusted, is put in use for messaging. At 1308 the method isdone.

FIG. 14 is a flowchart showing an exemplary embodiment of a method forselecting an improved modulation table, according to some embodiments.In this method, a network first accumulates data on network performanceand fault distributions with various operating conditions and variousmodulation tables in use, and then provides the data to a predictiveArtificial Intelligence structure. Using the data, certain variables inthe AI structure are iteratively adjusted to improve the predictiveaccuracy of the AI structure. For example, the AI structure couldpredict a network performance metric such as the fault rate or thethroughput minus the rate of retransmission. After the variables in theAI structure have been adjusted to obtain sufficiently accuratepredictions of the network performance metric, an algorithm is preparedbased on the AI structure, and the algorithm is distributed to thenetworks for use in selecting which modulation table to use in varioussituations.

At 1401, a base station or core network measures the current networkparameters of a cell or LAN. The measurements may include the currentmessage failure rate, the types of faults observed, the traffic density,and preferably numerous other network parameters, along with a record ofwhich modulation table or tables are in use. Preferably the fault datais recorded for each user node and each modulation table employed at thetime of each fault, including the type of fault detected. Otherparameters of interest, such as interference from outside the network,may be recorded at 1402. Preferably the data accumulation is continuedlong enough to record the network conditions and performance under awide range of conditions at 1402. The resulting network performance isthen recorded at 1403 including any faults. Then, optionally, at 1404the network may plan experiments, such as changing the modulation tableor tables in use, or the frequencies or bandwidths or other operationalparameters, and so forth. Alternatively, the network may continue tooperate normally while continuing to accumulate operational data. Theflow then returns to 1402 to take further data as the network respondsto any changes. Preferably a substantial database of network conditions,modulation conditions, and resulting performance metrics are accumulatedover time, including a wide range of conditions. Other data from othernetworks may be included, or provided in parallel, so as to expose theAI structure to as many different scenarios as are available in thedatabase. Periodically, or continually, the data may be transferred to acentral computer as indicated by a double arrow.

At 1405 a central computer receives the database and prepares (orobtains from elsewhere) an artificial intelligence AI structure, such asa neural net or hidden Markov model or other artificial intelligencemeans for processing network data and predicting subsequent performance.In particular, the AI structure may be configured to predict how eachmodulation table, of various available modulation tables, would likelyaffect the subsequent performance of the network. The AI structure takesin, as inputs, the network data and, preferably, combines it with datafrom a large number of other base stations as well, thereby to assemblea sufficient number of operational examples under a wide range ofnetwork conditions. The inputs of the AI structure may include theoperating conditions of the network, fault rates observed, andparameters of the modulation table in use (such as the numbers of levelsNamp and Nphase, the separations between them, and any invalid states inthe modulation table). The output or outputs may include one or morepredicted network performance metrics such as the throughput and failurerate. The intermediate functions are internal functions or routines thatperform mathematical and logical operations on the input data and/or onthe results of other intermediate functions. The output predictions arethen derived from certain of the intermediate functions, such as thelowest layer of intermediate functions. Variables (“values”) in theintermediate functions (and optionally the links between them) are thenadjusted to cause the predicted network performance metric to becomecloser to the observed metric, as in “supervised” learning based on theactual performance achieved by the network in each scenario. After theAI structure has achieved sufficient predictive accuracy to predict thesubsequent performance of networks to a predetermined accuracy, the AIstructure can then be used by the networks themselves, for example topredict how various modulation tables would perform under similarconditions, as well as many other useful tasks. The AI structure, orother calculation means derived from it, can then be used by the basestation or core network to compare different available modulation tablesfor suitability, in view of current network conditions. The predictionscan thereby enable network operators to select the most suitablemodulation table for subsequent use.

When the AI structure is first prepared, the variables are usually setat arbitrary values at first, and therefore the AI structure usuallygenerates extremely poor predictions before being tuned. The variablescan then be adjusted to bring the predicted outputs into better accordwith the observed data, which generally results in improved predictiveaccuracy when new conditions are presented. At 1406, the computer setsor adjusts the AI structure, specifically the weights, thresholds,biases, and optional logic of the intermediate functions, to optimizethe accuracy of the predictions. With those settings, at 1407 thecomputer uses the AI structure to predict the subsequent networkperformance metric such as the message failure rate, the throughput, andso forth. Then at 1408 the computer, or a supervisory processor,compares the predictions with the network observations. A success factormay be prepared according to the features that the AI structurepredicted correctly. The flow then returns to 1406 for more variationsof the internal values or further network operational histories. Thisprocess continues iteratively to refine the variables of theintermediate functions in order to optimize the success factor, orotherwise improve the predictions.

Upon each iteration, the values in the various intermediate functionsmay be adjusted to follow any improvements in predictive accuracy. Thedepicted process may be repeated many times using data from many basestations covering many different operational scenarios. Each successfulprediction may form the starting point for extensions to otherscenarios, a form of deep learning. In a particular iteration of themethod, the values of the intermediate functions may be adjusted in thesame direction that they were adjusted in the previous iteration if thepredictions were improved thereby, or in the opposite direction if thepredictions were less accurate. Alternatively, to avoid getting stuck onlocal peaks, an arbitrary large change in values may be imposed and thefine-tuning process repeated from that point. Decisions about whichvariables to vary, and which direction and by how much, may be random orpre-planned or based on the computer's previous experience with similarvariations. The iterative adjustment process with feedback from thenetwork data is thus a form of guided learning. If the computer decideswhich values to vary based on its previous experience, the process is anexample of recursive self-improvement.

The adjustment process is generally complex and arduous, requiringadvanced software and many hours on extremely competent supercomputers,due to the large number of tightly interacting variables in a problemsuch as network operation management. Nevertheless, systems exist thatcan handle such challenges and provide accurate predictions, givensufficient input data to work with. After a sufficiently successful AIstructure has been developed, at 1409 an algorithm may be prepared fromthe results. For example, the algorithm may be the AI structure itself,but with the intermediate function values preferably frozen, so thatpredictions can be obtained by inserting network operating parameters(such as a particular modulation table) as inputs and calculating theresulting predicted performance. Alternatively, the algorithm may be asimplified or compact version of the AI structure by, for example,pruning the unproductive intermediate functions or input parameters.Alternatively, the algorithm output may be configured to recommend aparticular modulation table directly, from a set of available modulationtables, instead of displaying predicted network performance metrics foreach table. In that case, the algorithm selects the best modulationtable and informs the network of the selection. The selection may bebased, for example, on which modulation table would likely provide thehighest message throughput or the lowest failure rate or the shortestaverage delays, of the available modulation tables, given the currentoperational parameters of the network. The algorithm may be prepared asa computer code, a formula, a table or matrix of values, or other formatcapable of rendering predictions or recommendations based on the inputparameters. The resulting algorithm may then, at 1410, be distributed tothe base stations for use in selecting or optimizing their modulationtables under various conditions.

FIG. 15 is a schematic of an exemplary AI structure, arranged in thisembodiment for predicting a network performance metric according toinput parameters. The depicted case is a neural net, but other AIconfigurations may be used instead. Variables in the AI structure may beadjusted to cause the predicted performance metric to predict anobserved performance metric, preferably with sufficient accuracy thatnetwork operators can use the results for selecting modulation tablesand other operational parameters. The input parameters 1505 include anumber (three shown) of network conditions 1501 such as the number ofuser nodes, the current traffic density, and an average message size,among many other possible network parameters potentially of interest.The input parameters 1505 also include parameters of a modulation table1502 such as the number of amplitude levels Namp, the number of phaselevels Nphase, noise widths in the amplitude and phase directions,amplitude level separations in the amplitude direction and phase levelseparations in the phase direction, and whether any of the modulationstates are invalid, among other modulation parameters potentially ofinterest. The input parameters 1505 also include information on messagefaults detected 1503 by the network, while operating with the networkconditions 1501 and modulation table 1502 as specified. The detectedfaults 1503 may include a number or rate of adjacent-amplitude faults inwhich a message symbol is distorted by a single amplitude level,adjacent-phase faults in which a symbol is distorted by a single phaselevel, and non-adjacent faults in which a symbol is distorted by morethan a single amplitude or phase level, among other possible faultparameters of interest. Each input parameter 1505 is indicated by acircle 1508. In other embodiments, the fault data 1503 is not providedas input to the AI structure, but rather is predicted by the AIstructure as a set of outputs along with other network performancemetrics. Artisans may design other AI structures with differentarchitectures after learning about the systems and methods disclosedherein.

The AI structure also includes a number of intermediate functions 1506,arranged in this embodiment as two layers under the inputs 1505,although other embodiments may have more layers and more intermediatefunctions per layer. The AI structure is arranged to produce an output1507 which in this embodiment is a predicted network performance metric1510. The schematic also shows a performance metric column 1504,including an observed network performance metric 1511, which is observedin a network with the network conditions and modulation table listed inthe inputs 1505. The performance metric 1511 may be, for example, themessage throughput minus ten times the failure rate minus five times theaverage delay per message, among many other forms of a performancemetric of interest to network operators.

Also shown are lines (“links”) 1509 connecting the inputs 1505, theintermediate functions 1506, and the output 1507. Each intermediatefunction 1506 is a function or subroutine or other means forcalculating, based on parameters obtained from the inputs 1505 or fromanother layer of the AI structure, and for feeding results of thecalculating to the next layer, and finally to the output 1507. Forclarity, the figure shows links connecting only a few of the functionsof each layer, but a real AI structure may have links from each functionconnecting all of the functions of the previous layer and in thesubsequent layer. Each of the intermediate functions 1506 includesadjustable variables. The variables may be adjusted to bring thepredicted performance metric 1510 into agreement with the observedperformance metric 1511 as indicated by the “compare” arrow. Forexample, a particular variable or set of variables in one or more of theintermediate functions 1506 may be adjusted in a first direction, andthe predicted and observed 1510-1511 metrics can be compared todetermine if the agreement is better or worse. If better, the adjustmentof variables may be repeated or increased in the same direction, and ifworse, the adjustment can be turned in the opposite direction. Theadjustment can be continued in this iterative fashion until theagreement is satisfactory, or other criterion. The links may also beweighted or otherwise include calculational processes besides that ofthe intermediate functions. The links in the example are “directed”downward toward the outputs, however other embodiments may includebidirectional links or links sending calculation results upward towardhigher layers, or other grid topologies, to address issues such as userresponse to message failures, among other issues in networkingexperience.

To provide predictions across a wide range of network scenarios, datafrom a large number of networks, at a large number of different times,may be applied as well. Each scenario may be used as an input model andtuned individually, thereby deriving a set of values for theintermediate function variables, and optionally the link variables aswell. Alternatively, an averaged (“clustered”) input may be derived frommultiple related scenarios, and the function variables may be adjustedto improve or optimize agreement between each predicted performancemetric and the corresponding observed performance metric. After a largenumber of different scenarios have been satisfactorily predicted, thevariable values derived from different scenarios may be combined, suchas averaged, to broaden the applicability of the solutions, and theresulting composite set of values may then be tested with additional,preferably novel to the structure, network data. The intent may be todevelop an AI structure capable of predicting the effect of changing amodulation table, as well as many other operational decisions, and maythereby assist networks in selecting a suitable modulation table andotherwise managing a torrent of user demands, according to theiroperating conditions.

When the AI structure has reached a sufficient accuracy, it may beconverted to an algorithm for convenient use by networks in selecting oradjusting a modulation table, among other network parameters. Often theAI structure itself is so large and complex, it may be unwieldy for useby a base station or core network in a busy network. Therefore, analgorithm can be configured as a handy and readily usable version of theAI table with sufficient predictive power for general networkdecision-making. The algorithm may be, for example, the AI structureitself but with the variables frozen at the best combination so farobtained (that is, the set of variables providing the closest agreementbetween the predicted and observed performance metric across asufficiently wide range of network conditions and a sufficiently widerange of modulation tables). Alternatively, the algorithm may be asimplified version of the AI structure in which certain inputs andintermediate functions are eliminated if they exhibit little or nocorrelation with the predicted performance metric. Alternatively, thealgorithm may be cast as an analytic function, a computer code, atabular array which may be multi-dimensional, or other means forproviding a predicted performance metric according to the networkconditions, modulation table, and faults detected. Networks(specifically base stations or core networks) can then use the algorithmto predict performance with each of a number of available modulationtables, determine which table is predicted to provide the bestperformance according to the metric, and then switch to that modulationtable.

FIG. 16 is a flowchart showing an exemplary method for using analgorithm based on an AI structure, such as that of FIG. 15. At 1601,parameters such as network conditions and observed fault rates are usedas inputs to the algorithm. A first modulation table is assumed. At 1602the algorithm is used to predict the performance metric for the inputscenario and with the first modulation table. Those steps may berepeated for a second modulation table, and for each of the modulationtables available to the network. After predicting performance metricsfrom all of the available modulation tables, at 1603 the best modulationtable (whichever modulation table would provide the highest predictedperformance metric) may be selected and activated in the network. Themethod may be repeated whenever network conditions change.

FIG. 17 is a schematic of an exemplary modulation table having avariable amplitude level, according to some embodiments. As in theprevious schematics, the amplitude is shown on the vertical axis and thephase on the horizontal axis, with each modulation state shown as a “+”1701 surrounded by a noise contour 1702. One of the amplitude levels,A2, is rendered at five different values, shown as dashed lines 1722corresponding to five settings of the A2 amplitude setting. The statesand noise contours for one exemplary setting of the A2 amplitude areshown in dash 1721. The A2 level may be set at any one of the fivevalues depicted. On the right are five bars 1723 indicating the networkperformance metric obtainable with each of the respective A2 levelsettings, respectively. The indicated network performance metrics 1723may be the predictions of an algorithm such as that of FIG. 16, or theymay be obtained experimentally by the network in trying out each of thelevel settings 1721. The longest bar (shown in the middle of thedistribution 1723 in this case) indicates that the best performance isobtainable by setting the A2 level at the indicated value. Apparentlythe other settings are either too high or too low, and thus are tooclose to the adjacent levels A1 and A3, resulting in more faults thanthe favored choice.

FIG. 18 is a flowchart showing an exemplary method for setting anamplitude modulation level, or other network parameter, using anAI-derived algorithm, according to some embodiments. At 1801, thenetwork conditions, faults detected including type, and a modulationtable including the current value of an adjustable amplitude level, areprovided as inputs to an algorithm. At 1802, the algorithm is used topredict a network performance metric by performing calculations or thelike, related to the inputs. Steps 1801 and 1802 are repeated aplurality of times, using a different value for the adjustable amplitudelevel, thereby obtaining a plurality of predicted network performancemetrics. At 1803, the best predicted network performance metric isdetermined and the associated amplitude level setting is determined. Themodulation table, with the amplitude level (or levels) so adjusted, isthen employed for wireless communications.

AI structures, such as that shown in FIG. 15 or described elsewhereherein, can provide a wide range of services to network operators. Forexample, AI structures can be adapted to solve allocation problems suchas determining which user node to provide resources based on trafficdensity, the size of the waiting messages, the priority of the waitinguser nodes, and many other factors that can change on a millisecondbasis. The network is responsible for allocating resources (transmissionopportunities and scheduling) to the highest priority user nodes first,yet without starving the low-priority user nodes, and providing asufficient response to each user. Some high-priority user nodes mayrequire very low latency, whereas others may require high reliabilitybut a more leisurely response, while others may require both speed andreliability. Some user nodes, such as massively parallel machine-typesensors and the like, may require few resources at infrequent intervals,yet may strain the network due to their large total numbers. Adjacentnetworks, sharing similar frequency bands, may cause interference withsome or all of the network's users. Beamforming may alleviate some ofthese problems but with added complexity, especially since each usernode will likely have different beam capabilities and a differentscattering environment. AI can help resolve many of these challenges, orat least provide workable options in many cases, by applying methods andstructures disclosed herein. After reading the examples taught above,artisans may develop AI structures for managing network scheduling toprovide optimal throughput, or other performance metric, under a widerange of operating conditions, by tuning the variables of the AIstructure to provide performance predictions or operational choicesaccording to real-world data acquired by the networks. In addition,artisans can condense a successfully developed AI structure into ausable algorithm, which the network operators can then use fordecision-making in realtime. In addition, the same networks can recordthe subsequent performance and operational parameters, and return thatdata for continuing development and updating of the AI structure, andthereby enable better networking in the future.

The systems and methods may be fully implemented in any number ofcomputing devices. Typically, instructions are laid out on computerreadable media, generally non-transitory, and these instructions aresufficient to allow a processor in the computing device to implement themethod of the invention. The computer readable medium may be a harddrive or solid state storage having instructions that, when run, orsooner, are loaded into random access memory. Inputs to the application,e.g., from the plurality of users or from any one user, may be by anynumber of appropriate computer input devices. For example, users mayemploy vehicular controls, as well as a keyboard, mouse, touchscreen,joystick, trackpad, other pointing device, or any other such computerinput device to input data relevant to the calculations. Data may alsobe input by way of one or more sensors on the robot, an inserted memorychip, hard drive, flash drives, flash memory, optical media, magneticmedia, or any other type of file-storing medium. The outputs may bedelivered to a user by way of signals transmitted to robot steering andthrottle controls, a video graphics card or integrated graphics chipsetcoupled to a display that maybe seen by a user. Given this teaching, anynumber of other tangible outputs will also be understood to becontemplated by the invention. For example, outputs may be stored on amemory chip, hard drive, flash drives, flash memory, optical media,magnetic media, or any other type of output. It should also be notedthat the invention may be implemented on any number of different typesof computing devices, e.g., embedded systems and processors, personalcomputers, laptop computers, notebook computers, net book computers,handheld computers, personal digital assistants, mobile phones, smartphones, tablet computers, and also on devices specifically designed forthese purpose. In one implementation, a user of a smart phone orWiFi-connected device downloads a copy of the application to theirdevice from a server using a wireless Internet connection. Anappropriate authentication procedure and secure transaction process mayprovide for payment to be made to the seller. The application maydownload over the mobile connection, or over the WiFi or other wirelessnetwork connection. The application may then be run by the user. Such anetworked system may provide a suitable computing environment for animplementation in which a plurality of users provide separate inputs tothe system and method.

Embodiments of the systems and methods disclosed herein can providenumerous advantages not obtainable from prior-art wireless protocols.Embodiments may provide increased throughput and/or reduced messagefailure rates by allowing nodes to transmit using asymmetric ornon-square modulation tables that mitigate specific types of faults,such as amplitude or phase faults, faults concentrated in certainportions of the modulation table, and pulsatile interference, among manyother possible fault characteristics. Network operators can use methodsand algorithms derived from operational data and, optionally, AImodeling, to select and fine-tune modulation table parameters as well asother network parameters to optimize performance in various ways.

In the coming years, the number of wireless networks and devices isexpected to increase exponentially as 5G is rolled out, increasing evenmore as future technologies such as 6G are developed. For this reason,the need for efficient utilization of the shared radio medium isexpected to become severe. Asymmetric modulation tables, implemented asdisclosed herein, may provide means for reducing message failures whileenhancing throughput, with AI-assisted selection and optimization of themodulation table for current network parameters, according to someembodiments.

It is to be understood that the foregoing description is not adefinition of the invention but is a description of one or morepreferred exemplary embodiments of the invention. The invention is notlimited to the particular embodiments(s) disclosed herein, but rather isdefined solely by the claims below. Furthermore, the statementscontained in the foregoing description relate to particular embodimentsand are not to be construed as limitations on the scope of the inventionor on the definition of terms used in the claims, except where a term orphrase is expressly defined above. Various other embodiments and variouschanges and modifications to the disclosed embodiment(s) will becomeapparent to those skilled in the art. For example, the specificcombination and order of steps is just one possibility, as the presentmethod may include a combination of steps that has fewer, greater, ordifferent steps than that shown here. All such other embodiments,changes, and modifications are intended to come within the scope of theappended claims.

As used in this specification and claims, the terms “for example”,“e.g.”, “for instance”, “such as”, and “like” and the terms“comprising”, “having”, “including”, and their other verb forms, whenused in conjunction with a listing of one or more components or otheritems, are each to be construed as open-ended, meaning that the listingis not to be considered as excluding other additional components oritems. Other terms are to be construed using their broadest reasonablemeaning unless they are used in a context that requires a differentinterpretation.

The invention claimed is:
 1. A method for predicting a networkperformance parameter, the method comprising: a. using, in a computer,an artificial intelligence array comprising a plurality of inputparameters, an output parameter, and a plurality of intermediatefunctions, each intermediate function depending functionally on one ormore of the input parameters, and wherein the output parameter dependsfunctionally on the intermediate values; b. measuring a number Famp ofadjacent-amplitude faults involving adjacent amplitude levels of amodulation table, a number Fphase of adjacent-phase faults involvingadjacent phase levels of the modulation table, and a number Fnonadjacentof non-adjacent faults involving non-adjacent amplitude or phase levelsof the modulation table; c. setting at least one of the input parametersaccording to Famp, Fphase and Fnonadjacent; d. setting at least oneadditional input parameter according to the modulation table; e.predicting, with the artificial intelligence structure, a predictedoutput parameter; f. comparing the predicted output parameter with ameasured network performance metric; and g. adjusting one or more of theintermediate functions until the predicted output parameter equals themeasured network performance metric within a predetermined fraction of1%, 5%, 10%, 20%, or 50% of the measured network performance metric. 2.The method of claim 1, wherein the network performance parameter isassociated with a wireless network configured to communicate accordingto 5G or 6G technology.
 3. The method of claim 1, wherein the setting atleast one additional input parameter according to the modulation tablecomprises either: a. setting the input parameter according to an integerNamp of amplitude levels in the modulation table; or b. setting theinput parameter according to an integer Nphase of phase levels in themodulation table.
 4. The method of claim 1, wherein the predictingcomprises calculating each of the intermediate functions according tothe input parameters, and then calculating the output parameteraccording to the intermediate functions.
 5. The method of claim 1,wherein the network performance parameter comprises a messagethroughput, a message failure rate, or combinations thereof.
 6. Themethod of claim 1, wherein the output parameter comprises a selectedmodulation table, selected from a set of predetermined modulationtables.
 7. The method of claim 1, wherein the output parameter comprisesan adjustment of an amplitude level or a phase level of the modulationtable.
 8. The method of claim 1, further comprising determining analgorithm, based at least in part on the artificial intelligence array,configured to calculate the output parameter according to the inputparameters.
 9. The method of claim 8, wherein the algorithm is at leastone of: a. the artificial intelligence array; b. an analytic formula; c.a computer program; d. a table or array; and e. combinations thereof.10. The method of claim 1, further comprising: a. producing an algorithmfrom the artificial intelligence array, the algorithm configured topredict the output parameter based at least in part on the additionalinput parameter; and b. transmitting the algorithm to the network. 11.The method of claim 10, further comprising: a. receiving, by thenetwork, the algorithm; b. predicting, by the network, a predictedoutput parameter according to each available modulation table of aplurality of available modulation tables; and c. selecting a particularavailable modulation table associated with a largest predicted outputparameter.